1. Business Analytics
A Strategic Imperative
SAS Forum India 2014
23rd April – Grand Hyatt, Mumbai

2. 1
• Introduction to Idea Cellular
• Data transforming world around us
– Data Analytics drivers & emerging trends
• Strategic importance of analytics for an Indian Telco
– Challenges faced by Indian Telcos
• Data Analytics at !dea
– CLM @ !dea
• Benefits
Contents

3. Introduction
to
Idea Cellular

4. 3
• Part of Aditya Birla Group
– $40Bn Global Conglomerate; presence in 36 countries
– Over 1.4 lac employees belonging to 42 different nationalities
• 3rd largest Pan India Mobile Service operator
– Listed; $ 8Bn + market capitalisation
– $ 4Bn + revenues & 600 Bn + minutes of usage per annum
– Ranks among top 10 country operators globally in terms of traffic
– 135 Mn + subscribers getting coverage from 1 lac + towers
– Leaders in Mobile Number Portability
– Ranked # 1 in telecom sector at Asia Communication Awards 2013
• ‘India’s Best Companies to Work for Study – 2013’ & ‘Best Place to Work’
Introduction – Idea Cellular

5. Data Transforming
World Around Us

6. 2009
2011
2015
2020
5
Global Data growing exponentially
0.8 ZB
1.9 ZB
7.9 ZB
35 ZB
CAGR (2009-20) 41.0%
Implication on an organisation
• Need for large storage capacity
• Need for quick retrieval of data
• Enable informed decision making
effectively, leveraging large datasets.
Data captured by organisations to understand customers, suppliers, partners & operations.
1 Zettabyte (ZB) = 1 Bn Terabyte (TB) Source : Nasscom Big data report

7. 6
Managing Volume, Variety & Velocity
Volume • Volume - Large quantity of data
• 22 Bn GB of data is generated everyday globally
• Variety - Diverse set of data
• Competition & Customer related
• Campaign & Channel related
• Social Networking feeds
• Velocity – Speed of data inflow
• 14 Bn + Mobile Minutes generated everyday in India
• 30 Mn + passengers travel from rail everyday in India
• 20 Mn + ATM /POS transactions everyday in India
• 17 Mn + internet searches everyday in India
Source : RBI & comScore Indian Digital Future 2013

8. Key Industries using Data Analytics
Financial
Services
Retail Healthcare /Pharma Manufacturing Telecom
7
Data Analytics across Industries
•Claims & Renewal
Analytics
•Sales Force
Analytics
•Collection &
Recovery
Scorecards
•Portfolio
Analytics
•Pricing & Risk
Analytics
•Demand
Forecasting
•Marketing Mix
Analytics
•Performance
Analysis
•Category
Management
•Trade Promotion
Optimization
•Evidence Based
Medicine
•Drug Treatment
Effectiveness
•Clinical Analytics
•Average Length of
Stay
•Key Opinion
Analysis
• Collection
Management
• Subscriber Profiling
• Competition
Benchmarking
• Churn Management
• Revenue Assurance
• Customized
Offerings & Up-
Selling
• Demand
Forecasting & SKU
Rationalisation
• Media ROI
Optimizations
• Route &
Distribution
Optimization
• Vendor
Performance
Management

9. 8
Drivers for Data Analytics
• Sales Reporting & Tracking
• Cost Reduction
• Risk Management
• Better view to Financial data
• Regulatory Compliance
Drivers for
BI & Analytics
• Innovation
• Competitive Differentiation
• Reducing costs & Increasing Efficiencies
• Growth
• Insights for future strategy
Organization
Benefits

10. • Increased focus on Predictive Analytics
– Historical events Vs forecasting future trends
• Real Time Analytics
– Quicker decision making with help of real time data.
• Social Media Analytics
– Focus on deriving customer insights based on social media behaviour
– Real time inputs from Facebook, Twitter, Linkedln etc.
• Integration of ERP & Analytics Software
– Integration of data generation and data analysis through BI mart
• Drive appropriate Variables & KPIs for enhanced business results
9
Emerging Trends in Data Analytics

11. Data Analytics
@
Indian Telco

12. 1. Hypercompetitive landscape ; 12 operators across & 6- 9 operators / circle
– Price war; small operators operating at half rates compared to big ones
11
Indian Telcos – Challenges (1/2)
49.4
35.1
32.0
23.5
14.6
9.2
6.6
3.1 3.0 2.9
ARPU (USD)
15.9
4.9 6.5 6.1
9.1
4.3
10.8
10
4.1
2,300 1,700 1,401 1,215 717 465 440 278 242
Usage/Sub (MB) Price/GB (USD)
2. Low ARPUs & low rates to global standards
3. Low entry /exit barrier for customers
• High acquisition - High churn market

13. 4. Hyperactive Regulatory
– EMF# regulations, various penalties (form related & telemarketing)
– New acquisition guidelines; increased cost of acquisition by 20%
– TCPR* guidelines; leading to revenue erosion of $300 Mn+.
5. Artificial Spectrum scarcity leading to high auction bids and increased debt
– 3G & 4G auctions, one of the most expensive globally, $22Bn +
– Spectrum charges & license fees, even after acquiring spectrum through auction
– ROI < 1%, Net Debt to EBITDA ratio 4.5 & Consolidated Gross Block – $120 Bn
12
Indian Telcos – Challenges (2/2)
*Telecom Consumer Protection Regulation
# Electro Magnetic Force

14. Size
• 2nd largest in the world, after China
• 900 Mn Mobile users; 200 Mn Mobile Internet users & 40 Mn Smartphones
Diversity
• 22 circles or service areas ranging from 7 Mn to 70 Mn subs
• Tele-density varying from 50% to 240% !!!
Growth
• Net additions of 6 – 7 Mn every month
• Data traffic growth 90% overall in 2013; 150% for 3G
13
Telecom Market Scenario in India

15. • 14 Bn Voice minutes generated in a day
• On-Net, Off-Net, Landline, STD, ISD, Roaming, Toll-free, Video
• 2.5 Bn MBs data generated in a day
• Billions of charging instances everyday
• 20 Mn customer care calls everyday
• 2 Mn retailers catering subscribers everyday
• 6.5 Lac telecom towers covering 4 Lac Population centres
• Market share fought at every tower
• 40% Google searches & 30% Facebook users are mobile only
14
Volume- Variety- Velocity
for Indian Telcos

16. Typical lifecycle of a Telco customer
Phase 1:
New joiner phase
Phase 2:
Stable phase Phase 3:
Churn phase
Time (AoN)
Revenue
▪ Low entry barrier
▪ Rotational Churn
▪ Low exit barrier

17. Data Analytics
@
!dea

18. 17
Evolution of Analytics in !dea
•Predefined Static reports
•Day and Month wise
reports
•Reports based on data from
transactional systems
•Drill down hierarchy
reports
• Time
• Geography
• Age on Network
• Slicing & dicing of reports
• Incoming/Outgoing calls
• On-net, Offnet, STD, ISD,
Roaming
• Scorecards & Dashboards
• Analyzing KPIs & monitoring
trends, through graphs /
charts with event based
alerts
• Prediction Analysis
• Customer Churn
• Revenue drop
•Competition Tracking – Site
wise
• Acquisitions
• Net adds
• Traffic
• Customer lifecycle
management
MIS Analytics Advanced analytics
Core function under CMO

19. Data Analytics @ !dea
Competition
Related
Customer
Related
Campaign
Related
Service
Related
Channel
Related
18
Site wise
• SOGA – Share of
Gross Adds
•SONA – Share of
Net Adds
•RMS – Revenue
Market Share
•Usage – Minutes
& Data
•Product based
Segmentation
•Usage based
Segmentation
• Call Leg based
segmentation
• Geography based
segmentation
• AoN based
segmentation
• Dynamic Churn
based programs
• Revenue
enhancement
programs
• Cross- sell &
Up-sell programs
• Brand Track
Index
• Channel Satisfaction
• Activation &
Recharge based
Retailer
Segmentation
• Geography & AoN
based Distributor
Segmentation
• Channel
commissions &
incentives
• Customer
Satisfaction
• Calls @ Call
centre
• Walk ins @
showrooms
• Collection
Management
• Activation
Management

20. Data Analytics @ !dea
Way Forward
Data Platform Near Real Time
Analytics
Near Real Time
Promotion
Map Platform
Integration
Visualization
19
• Social Media
Analytics
• Probe based URL
analytics
• Customer
Experience
Management
• Customer
Profiling and
Monetization
• Near Real Time
Data streaming
• Near Real Time
Event Processing
• Near Real Time
Analytical Models
• Location Based
offers / Ads
• Offers Based on
recent experience
/ Behavior
• Cross sell / Up sell
Offers Based on
Recharges /
Subscriptions
• Ability to process
high data volume
without
preprocessing
using IN memory
and Associative
features
• Ability to get
business Insights
before developing
regular KPI
• Display of key
business KPIs on
Map
• Ability to highlight
Hotspots for easy
visual detection
• Drill Up/ Drill
Down
• Switch Between
MAP and Tabular
display

21. Customer Lifecycle Management
@
!dea

22. Industrialised systems and processes
 Measuring real time campaign impact on
top-line and bottom-line
 Clear Targets

Campaign library & product catalogues
to drive competitive edge; !dea IPR

CLM Objectives & Deliverables
21

23. Test offers against each segment to find positive contribution to
bottom line. Let customer decide the best offer !!!
Micro segments (50-100K) based on similarity of usage behaviour
through intensive data mining. Exclusive offers to each micro-
segment to prevent value destruction !!!
Target customers at all stage of lifecycle. Up-sell, cross-sell, retain
and train through creative offerings. !!!
Tested offers to be scaled to entire set of micro-segments &
analyze results on real time basis. Build library of performing
offers !!!
Micro-
Segmentation
2
Creative
campaigns
4
Scale & Speed
Don’t guess,
Test !
1
3
How CLM in !dea is different?
22

24. Usage Leg based segmentation
Value
levers
Share of
Wallet
Usage
stimulation
Retention
Type (SMS, Voice, VAS)
Time (Weekend, Night))
Volume (MoU, Count)
Duration/Frequency
Outgoing/Incoming
Primary dimension
Primary usage Sub-usage
>3
mths
Internet/
text users
Internet/
text non
users
Mr. Local
Mr. STD
Mr. VAS
Mr. Balanced
xx
xx
xx
xx
xx
xx
High
Users
< 3
mths
xx
xx
AONMOU/VLR
All
subs
Mr. STD
Mr. VAS
Mr. Balanced
xx
xx
Mr. Local
Micro Segmentation Approach
23
xx
xx
Campaigns targeting value levers

25. Automation Advantages
• Create & analyze segments with more than 1400 variable options.
– More than 4000 campaigns launched in a month.
• Automation of communication at touch points
– DND scrubbing, scheduling, script banks & vernacular scripts
• Automated tracking of promotions
– 15 Mn+ subscribers doing 25 Mn+ segmented recharges valuing $ 22 Mn +
• Ability to create ‘Dynamic Campaigns’
• Creation of ‘Recurring Campaigns’

26. 25
Total Impact (ROI) of the offer
Not considered right now
ROI
• Incremental
Contribution
Gross Revenue
• Incremental
Revenue
• Loyalty
Benefits
Costs
• Incremental
Direct Costs
• Incremental
Network
Costs
• Execution
Costs

27. Customer Lifecycle Management
Phase 1:
New joiner phase Phase 2:
Stable phase
Phase 3:
Churn phase
Time (AoN)
Revenue
2% - 3% gain
6% - 10% gain
4% - 5% gain
▪ Reduce churn
▪ Increase ARPU ▪ ARPU stimulation
▪ Predictive Churn
▪ Recovery Programs
▪ Usage stimulation

28. Results
• 60% revenue base covered under CLM
• $ 100 Mn Revenue uplift
• 30% EBITDA

29. Thanks